{
  "abstract": "The hydrated proton plays a central role in aqueous chemistry, biological proton transport, and electrochemical energy conversion. Reliable simulations of hydratedproton properties require an accurate reactive potential with low computational cost. Here, we report HP-AQUA, a high-level many-body machine-learned potential for the hydrated proton with fast analytical gradients. HP-AQUA combines the CCSD(T) quality q-AQUA-pol water potential and an accurate H3O + monomer potential with new permutationally invariant polynomial representations of the hydronium-water two-, three-, and four-body interactions. HP-AQUA demonstrates CCSD(T)-level accuracy across a series of gas-phase protonated water clusters for binding energies, harmonic frequencies, proton-transfer profiles, and configurations sampled along molecular dynamics trajectories. Analytical gradients implemented through reverse differentiation reduce the computational cost by more than an order of magnitude relative to our previous",
  "abstract_shown": true,
  "affiliations": [
    "Department of Chemistry, Shanghai Key Laboratory of Electrochemical and Thermochemical Conversion for Resources Recycling, State Key Laboratory of Porous Materials for Separation and Conversion",
    "Fudan University",
    "Department of Chemistry",
    "Emory University and Cherry L. Emerson Center for Scientific Computation"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Guorui Zhang"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Ruitao Ma"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Joel M. Bowman"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Qi Yu",
      "orcid": "0000-0002-2030-0671"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2cw31fbw2ygyb.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15009682/v1",
  "fulltext_url": null,
  "id": "2cw31fbw2ygyb",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009682/v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7214969996",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-29",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/2cw31fbw2ygyb.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:21Z",
      "native_id": "10.26434/chemrxiv.15009682",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009682/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Many-body machine-learned potential for the hydrated proton at CCSD(T) accuracy with fast analytical gradient",
  "title_text": "Many-body machine-learned potential for the hydrated proton at CCSD(T) accuracy with fast analytical gradient",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:21Z",
  "url": "https://research.draconitic.com/paper/2cw31fbw2ygyb",
  "venue": "ChemRxiv",
  "versions": []
}